The present invention generally relates to signal processing techniques, and more particularly relates to receiver architectures for converting signals appearing across a wide observation bandwidth to sampled signals processed at a relatively low sampling rate.
Many signal formats and related protocols are used by communication systems and other radio-frequency (RF) and optical communication systems and devices. In some applications, such as electronics intelligence (ELINT) and signal intelligence (SIGINT) applications, simply determining or confirming the existence of signals is of value, although being able to measure, demodulate, and/or decode the signals is typically a goal.
However, in some of these applications the signals of interest might appear in any or several portions of a very wide band of frequencies. Further, the center frequencies of these signals are frequently unknown, and many of the signals may be obscured by noise. Accordingly, significant efforts have been directed to the design of receiver front ends having very wide “surveillance” bandwidths, i.e., receivers that are capable of continuous detection and observation of signals across a very wide spectrum. Ideally, such a receiver front end can simultaneously acquire several signals of various bandwidths across a very wide frequency range, e.g., tens of gigahertz (GHz), and convert those signals to digitally sampled signals that can be processed at relatively moderate rates, e.g., at tens or hundreds of megahertz (MHz), without adding undue noise and interference to the signals.
For very wideband applications, sampling at the Nyquist rate (twice the total signal bandwidth) can be impractical or impossible. Several sub-Nyquist sampling schemes have been developed, including non-uniform sampling techniques. However, non-uniform sampling techniques have generally been limited in the types and number of signals that can be processed.
A technology referred to as Compressive Sensing (CS) has recently generated significant interest in the signal processing and information theory fields. One architecture in particular, called a “modulated wideband converter” (MWC), has demonstrated impressive sub-Nyquist performance, and is described in Mishali and Eldar, “From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals,” IEEE Journal of Selected Topics in Signal Process, Vol. 4, No. 2, April 2010.
With the approach described by Mishali and Eldar, a very wide surveillance bandwidth is conceptually divided into several contiguous frequency bands. Fast pseudorandom sampling is then used to downconvert a wide spectral range to a much lower frequency and bandwidth, via aliasing. The known pseudorandom sampling waveforms generate “intelligent” aliasing of the contiguous frequency bands in the scanned spectrum. As a result, while these multiple contiguous frequency bands are downconverted so as to occupy a single, smaller bandwidth, it is still possible to distinguish between signals that originally occupied distinct frequencies in the surveillance bandwidth.
More particularly, the signals downconverted with the pseudorandom sampling waveforms are converted into high bit-depth digital samples, which are post-processed to reconstruct any relatively narrowband signals included in the original surveillance bandwidth. The resulting narrowband digital signals can be processed using conventional signal processing techniques to measure, demodulate, and/or decode the signals.
The compressive sensing techniques described by Mishali and Eldar suggest the possibility of major advances for wideband receivers. In particular, these techniques suggest that it may be possible to develop practically realizable receivers that persistently monitor extremely wide bandwidths (e.g., tens or hundreds of GHz) with high dynamic range. However, further improvements to these techniques are needed to make such receivers small, efficient, and cost-effective.